Details zum E-Book

Practical Machine Learning. Learn how to build Machine Learning applications to solve real-world data analysis challenges with this Machine Learning book – packed with practical tutorials

Practical Machine Learning. Learn how to build Machine Learning applications to solve real-world data analysis challenges with this Machine Learning book – packed with practical tutorials

Sunila Gollapudi

E-book
This book explores an extensive range of machine learning techniques uncovering hidden tricks and tips for several types of data using practical and real-world examples. While machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles. Inside, a full exploration of the various algorithms gives you high-quality guidance so you can begin to see just how effective machine learning is at tackling contemporary challenges of big data

This is the only book you need to implement a whole suite of open source tools, frameworks, and languages in machine learning. We will cover the leading data science languages, Python and R, and the underrated but powerful Julia, as well as a range of other big data platforms including Spark, Hadoop, and Mahout. Practical Machine Learning is an essential resource for the modern data scientists who want to get to grips with its real-world application.

With this book, you will not only learn the fundamentals of machine learning but dive deep into the complexities of real world data before moving on to using Hadoop and its wider ecosystem of tools to process and manage your structured and unstructured data.

You will explore different machine learning techniques for both supervised and unsupervised learning; from decision trees to Naïve Bayes classifiers and linear and clustering methods, you will learn strategies for a truly advanced approach to the statistical analysis of data. The book also explores the cutting-edge advancements in machine learning, with worked examples and guidance on deep learning and reinforcement learning, providing you with practical demonstrations and samples that help take the theory–and mystery–out of even the most advanced machine learning methodologies.
  • 1. Introduction to Machine learning
  • 2. Context of Large datasets for Machine learning
  • 3. Hadoop as a Machine learning platform
  • 4. ML tools and frameworks (R, Mahout, Julia, Spark and Python)
  • 5. Decision Tree learning methods
  • 6. Instance based & Kernel learning methods (KNN and SVM)
  • 7. Association rule based learning methods (Apriori& FP-growth)
  • 8. Clustering based learning methods (K-means)
  • 9. Supervised & Unsupervised Learning: Linear Methods
  • 10. Unsupervised Learning: Clustering Methods
  • 11. Deep Learning Methods
  • 12. Reinforcement learning
  • 13. Summary of all the large scale machine learning frameworks and tools
  • 14. Looking Ahead: Lamda Architectures, Polyglot Persistence and Semantic Data Platforms for Machine Learning
  • Titel: Practical Machine Learning. Learn how to build Machine Learning applications to solve real-world data analysis challenges with this Machine Learning book – packed with practical tutorials
  • Autor: Sunila Gollapudi
  • Originaler Titel: Practical Machine Learning. Learn how to build Machine Learning applications to solve real-world data analysis challenges with this Machine Learning book – packed with practical tutorials
  • ISBN: 9781784394011, 9781784394011
  • Veröffentlichungsdatum: 2016-01-30
  • Format: E-book
  • Artikelkennung: e_3css
  • Verleger: Packt Publishing